In a service delivery platform with real-time streaming data architectures, numerous data producers and data consumers may perform reads and writes simultaneously. The server delivery platform may contain service domains with multiple servers in data centers across distinct geographical areas with replications to keep them synchronized. Servers may be added to or removed from the service domains at any given time. Conventional systems attempt to dump server metric data into persistent stores and query the data for server status. However, due to the overwhelming size of the metric data and the lack of the capability to dynamically discover the servers, these systems fail to provide system wide insights into the servers, services or domains within reasonable response times. In addition, such persistent stores may not have a sophisticated aggregation process that application logics may need to be built on top of the queries, thereby limiting its ability to provide an accurate system wide status from top down into the subcomponents.
Aspects described herein may address these and other problems, and generally improve the flexibility, efficiency, and speed of processing metric data to offer insights into the details of the real time streaming data platform and aggregated service status.